// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H

namespace Eigen {

/** \class TensorForcedEval
 * \ingroup CXX11_Tensor_Module
 *
 * \brief Tensor reshaping class.
 *
 *
 */
namespace internal {
template<typename XprType, template<class> class MakePointer_>
struct traits<TensorEvalToOp<XprType, MakePointer_>>
{
	// Type promotion to handle the case where the types of the lhs and the rhs are different.
	typedef typename XprType::Scalar Scalar;
	typedef traits<XprType> XprTraits;
	typedef typename XprTraits::StorageKind StorageKind;
	typedef typename XprTraits::Index Index;
	typedef typename XprType::Nested Nested;
	typedef typename remove_reference<Nested>::type _Nested;
	static const int NumDimensions = XprTraits::NumDimensions;
	static const int Layout = XprTraits::Layout;
	typedef typename MakePointer_<Scalar>::Type PointerType;

	enum
	{
		Flags = 0
	};
	template<class T>
	struct MakePointer
	{
		// Intermediate typedef to workaround MSVC issue.
		typedef MakePointer_<T> MakePointerT;
		typedef typename MakePointerT::Type Type;
	};
};

template<typename XprType, template<class> class MakePointer_>
struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
{
	typedef const TensorEvalToOp<XprType, MakePointer_>& type;
};

template<typename XprType, template<class> class MakePointer_>
struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_>>::type>
{
	typedef TensorEvalToOp<XprType, MakePointer_> type;
};

} // end namespace internal

template<typename XprType, template<class> class MakePointer_>
class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors>
{
  public:
	typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
	typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
	typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
	typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
	typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
	typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
	typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;

	static const int NumDims = Eigen::internal::traits<TensorEvalToOp>::NumDimensions;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
		: m_xpr(expr)
		, m_buffer(buffer)
	{
	}

	EIGEN_DEVICE_FUNC
	const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

	EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }

  protected:
	typename XprType::Nested m_xpr;
	PointerType m_buffer;
};

template<typename ArgType, typename Device, template<class> class MakePointer_>
struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
{
	typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
	typedef typename ArgType::Scalar Scalar;
	typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
	typedef typename XprType::Index Index;
	typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
	typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
	static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
	typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
	typedef StorageMemory<CoeffReturnType, Device> Storage;
	typedef typename Storage::Type EvaluatorPointerType;
	enum
	{
		IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
		PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
		BlockAccess = true,
		PreferBlockAccess = false,
		Layout = TensorEvaluator<ArgType, Device>::Layout,
		CoordAccess = false, // to be implemented
		RawAccess = true
	};

	static const int NumDims = internal::traits<ArgType>::NumDimensions;

	//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
	typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
	typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

	typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;

	typedef internal::TensorBlockAssignment<CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index>
		TensorBlockAssignment;
	//===--------------------------------------------------------------------===//

	EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
		: m_impl(op.expression(), device)
		, m_buffer(device.get(op.buffer()))
		, m_expression(op.expression())
	{
	}

	EIGEN_STRONG_INLINE ~TensorEvaluator() {}

	EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }

	EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar)
	{
		EIGEN_UNUSED_VARIABLE(scalar);
		eigen_assert(scalar == NULL);
		return m_impl.evalSubExprsIfNeeded(m_buffer);
	}

#ifdef EIGEN_USE_THREADS
	template<typename EvalSubExprsCallback>
	EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType scalar, EvalSubExprsCallback done)
	{
		EIGEN_UNUSED_VARIABLE(scalar);
		eigen_assert(scalar == NULL);
		m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
	}
#endif

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) { m_buffer[i] = m_impl.coeff(i); }
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i)
	{
		internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(
			m_buffer + i,
			m_impl.template packet < TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned > (i));
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
	{
		return m_impl.getResourceRequirements();
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlockDesc& desc, TensorBlockScratch& scratch)
	{
		// Add `m_buffer` as destination buffer to the block descriptor.
		desc.template AddDestinationBuffer<Layout>(
			/*dst_base=*/m_buffer + desc.offset(),
			/*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()));

		ArgTensorBlock block = m_impl.block(desc, scratch, /*root_of_expr_ast=*/true);

		// If block was evaluated into a destination buffer, there is no need to do
		// an assignment.
		if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
			TensorBlockAssignment::Run(
				TensorBlockAssignment::target(
					desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()), m_buffer, desc.offset()),
				block.expr());
		}
		block.cleanup();
	}

	EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_buffer[index]; }

	template<int LoadMode>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
	{
		return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
	{
		// We assume that evalPacket or evalScalar is called to perform the
		// assignment and account for the cost of the write here.
		return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
	}

	EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_buffer; }
	ArgType expression() const { return m_expression; }
#ifdef EIGEN_USE_SYCL
	// binding placeholder accessors to a command group handler for SYCL
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const
	{
		m_impl.bind(cgh);
		m_buffer.bind(cgh);
	}
#endif

  private:
	TensorEvaluator<ArgType, Device> m_impl;
	EvaluatorPointerType m_buffer;
	const ArgType m_expression;
};

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
